Abstract
Objective: To investigate the clinical value of ADNEX model in early diagnosis and staging of benign and malignant ovarian tumors. Method: 136 cases of ovarian cancer patients treated in our hospital were retrospectively analyzed using the ADNEX risk model and MRI data. The accuracy of the two diagnostic methods was compared with the results of pathological examination as gold standard. Results: For qualitative assessment, the accuracy and sensitivity of the ADNEX model were 78.70% and 93%, while the accuracy and sensitivity of MRI examination were 80.1%, and 90.7%, respectively. The diagnostic values of the two methods were not statistically different (P > 0.05). For ovarian tumor staging, the ADNEX model was significantly less accurate and specific for staging borderline tumor than MRI examination, although it had significantly higher sensitivity (P 0.05). Conclusion: ADNEX risk model has certain diagnostic and predictive value to distinguish benign from malignant ovarian tumors. It is useful to detect and exclude ovarian tumor. However, for early diagnosis, it is not accurate enough and further study is needed to validate this usefulness.
Highlights
The incidence of ovarian cancer ranks the third in gynecological malignancies with the highest mortality [1]
ADNEX risk model has certain diagnostic and predictive value to distinguish benign from malignant ovarian tumors
In 2014, the Assessment of Different NEoplasias in the adneXa (ADNEX) model was proposed to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumors [7]
Summary
The incidence of ovarian cancer ranks the third in gynecological malignancies with the highest mortality [1]. To maximize the efficiency of early diagnosis of ovarian cancer, a number of ultrasound models have been proposed [4] [5] [6]. In 2014, the Assessment of Different NEoplasias in the adneXa (ADNEX) model was proposed to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumors [7]. It can automatically provide differentiation between benign and malignant and tumor staging information on mobile devices or websites using clinical information and ultrasound data. The aim of this study is to investigate the clinical value of the model in the early diagnosis and staging of benign and malignant ovarian tumors
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